{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入模块\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019162542\\t/front-api/bill/create\\t8\\t1057.31...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162644\\t/front-api/bill/create\\t5\\t749.12\\t103...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>162742\\t/front-api/bill/create\\t5\\t845.84\\t136...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>162808\\t/front-api/bill/create\\t9\\t1305.52\\t90...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>162943\\t/front-api/bill/create\\t3\\t568.89\\t138...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   0\n",
       "0  2019162542\\t/front-api/bill/create\\t8\\t1057.31...\n",
       "1  162644\\t/front-api/bill/create\\t5\\t749.12\\t103...\n",
       "2  162742\\t/front-api/bill/create\\t5\\t845.84\\t136...\n",
       "3  162808\\t/front-api/bill/create\\t9\\t1305.52\\t90...\n",
       "4  162943\\t/front-api/bill/create\\t3\\t568.89\\t138..."
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./log.txt', header=None)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019162542</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162644</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>162742</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:02:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>162808</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:03:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>162943</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:04:07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            0                       1  2        3       4       5      6   7  \\\n",
       "0  2019162542  /front-api/bill/create  8  1057.31   88.75  177.72  132.0  60   \n",
       "1      162644  /front-api/bill/create  5   749.12  103.79  240.38  149.0  60   \n",
       "2      162742  /front-api/bill/create  5   845.84  136.31  225.73  169.0  60   \n",
       "3      162808  /front-api/bill/create  9  1305.52   90.12  196.61  145.0  60   \n",
       "4      162943  /front-api/bill/create  3   568.89  138.45  232.02  189.0  60   \n",
       "\n",
       "                     8  \n",
       "0  2018-11-01 00:00:07  \n",
       "1  2018-11-01 00:01:07  \n",
       "2  2018-11-01 00:02:07  \n",
       "3  2018-11-01 00:03:07  \n",
       "4  2018-11-01 00:04:07  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./log.txt', header=None, sep='\\t')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 加上列名\n",
    "df.columns = ['id', 'api', 'count', 'res_time_sum', 'res_time_min', 'res_time_max', 'res_time_avg', 'interval', 'created_at']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>api</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>interval</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019162542</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162644</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>162742</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:02:07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id                     api  count  res_time_sum  res_time_min  \\\n",
       "0  2019162542  /front-api/bill/create      8       1057.31         88.75   \n",
       "1      162644  /front-api/bill/create      5        749.12        103.79   \n",
       "2      162742  /front-api/bill/create      5        845.84        136.31   \n",
       "\n",
       "   res_time_max  res_time_avg  interval           created_at  \n",
       "0        177.72         132.0        60  2018-11-01 00:00:07  \n",
       "1        240.38         149.0        60  2018-11-01 00:01:07  \n",
       "2        225.73         169.0        60  2018-11-01 00:02:07  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>api</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>interval</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>73435</th>\n",
       "      <td>5731853</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>8</td>\n",
       "      <td>1272.24</td>\n",
       "      <td>73.48</td>\n",
       "      <td>278.46</td>\n",
       "      <td>159.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-01-25 17:27:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142005</th>\n",
       "      <td>10542643</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>8</td>\n",
       "      <td>1572.20</td>\n",
       "      <td>148.33</td>\n",
       "      <td>303.21</td>\n",
       "      <td>196.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-04-18 15:14:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74582</th>\n",
       "      <td>5803221</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>8</td>\n",
       "      <td>1703.69</td>\n",
       "      <td>85.75</td>\n",
       "      <td>372.88</td>\n",
       "      <td>212.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-01-26 22:34:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60826</th>\n",
       "      <td>4894943</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>16</td>\n",
       "      <td>2697.43</td>\n",
       "      <td>95.28</td>\n",
       "      <td>509.92</td>\n",
       "      <td>168.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-01-10 21:38:14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143553</th>\n",
       "      <td>10663606</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>1</td>\n",
       "      <td>62.11</td>\n",
       "      <td>62.11</td>\n",
       "      <td>62.11</td>\n",
       "      <td>62.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-04-20 11:51:37</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              id                     api  count  res_time_sum  res_time_min  \\\n",
       "73435    5731853  /front-api/bill/create      8       1272.24         73.48   \n",
       "142005  10542643  /front-api/bill/create      8       1572.20        148.33   \n",
       "74582    5803221  /front-api/bill/create      8       1703.69         85.75   \n",
       "60826    4894943  /front-api/bill/create     16       2697.43         95.28   \n",
       "143553  10663606  /front-api/bill/create      1         62.11         62.11   \n",
       "\n",
       "        res_time_max  res_time_avg  interval           created_at  \n",
       "73435         278.46         159.0        60  2019-01-25 17:27:40  \n",
       "142005        303.21         196.0        60  2019-04-18 15:14:35  \n",
       "74582         372.88         212.0        60  2019-01-26 22:34:42  \n",
       "60826         509.92         168.0        60  2019-01-10 21:38:14  \n",
       "143553         62.11          62.0        60  2019-04-20 11:51:37  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(179496, 9)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                int64\n",
       "api              object\n",
       "count             int64\n",
       "res_time_sum    float64\n",
       "res_time_min    float64\n",
       "res_time_max    float64\n",
       "res_time_avg    float64\n",
       "interval          int64\n",
       "created_at       object\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 179496 entries, 0 to 179495\n",
      "Data columns (total 9 columns):\n",
      " #   Column        Non-Null Count   Dtype  \n",
      "---  ------        --------------   -----  \n",
      " 0   id            179496 non-null  int64  \n",
      " 1   api           179496 non-null  object \n",
      " 2   count         179496 non-null  int64  \n",
      " 3   res_time_sum  179496 non-null  float64\n",
      " 4   res_time_min  179496 non-null  float64\n",
      " 5   res_time_max  179496 non-null  float64\n",
      " 6   res_time_avg  179496 non-null  float64\n",
      " 7   interval      179496 non-null  int64  \n",
      " 8   created_at    179496 non-null  object \n",
      "dtypes: float64(4), int64(3), object(2)\n",
      "memory usage: 12.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                  179496\n",
       "unique                 179496\n",
       "top       2019-01-03 13:38:00\n",
       "freq                        1\n",
       "Name: created_at, dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['created_at'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>interval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.794960e+05</td>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.877739e+06</td>\n",
       "      <td>7.175909</td>\n",
       "      <td>1393.177832</td>\n",
       "      <td>108.419626</td>\n",
       "      <td>359.880374</td>\n",
       "      <td>187.812208</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.012494e+06</td>\n",
       "      <td>4.325160</td>\n",
       "      <td>1499.486073</td>\n",
       "      <td>79.640693</td>\n",
       "      <td>638.919827</td>\n",
       "      <td>224.464813</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.626440e+05</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>36.550000</td>\n",
       "      <td>3.210000</td>\n",
       "      <td>36.550000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.825233e+06</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>607.707500</td>\n",
       "      <td>83.410000</td>\n",
       "      <td>198.280000</td>\n",
       "      <td>144.000000</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.811510e+06</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1154.905000</td>\n",
       "      <td>97.120000</td>\n",
       "      <td>256.090000</td>\n",
       "      <td>167.000000</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.981455e+06</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1834.117500</td>\n",
       "      <td>116.990000</td>\n",
       "      <td>374.410000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.019163e+09</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>142650.550000</td>\n",
       "      <td>18896.640000</td>\n",
       "      <td>142468.270000</td>\n",
       "      <td>71325.000000</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id          count   res_time_sum   res_time_min  \\\n",
       "count  1.794960e+05  179496.000000  179496.000000  179496.000000   \n",
       "mean   6.877739e+06       7.175909    1393.177832     108.419626   \n",
       "std    6.012494e+06       4.325160    1499.486073      79.640693   \n",
       "min    1.626440e+05       1.000000      36.550000       3.210000   \n",
       "25%    3.825233e+06       4.000000     607.707500      83.410000   \n",
       "50%    6.811510e+06       7.000000    1154.905000      97.120000   \n",
       "75%    9.981455e+06      10.000000    1834.117500     116.990000   \n",
       "max    2.019163e+09      31.000000  142650.550000   18896.640000   \n",
       "\n",
       "        res_time_max   res_time_avg  interval  \n",
       "count  179496.000000  179496.000000  179496.0  \n",
       "mean      359.880374     187.812208      60.0  \n",
       "std       638.919827     224.464813       0.0  \n",
       "min        36.550000      36.000000      60.0  \n",
       "25%       198.280000     144.000000      60.0  \n",
       "50%       256.090000     167.000000      60.0  \n",
       "75%       374.410000     202.000000      60.0  \n",
       "max    142468.270000   71325.000000      60.0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop('api', axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>interval</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019162542</td>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162644</td>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>162742</td>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:02:07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id  count  res_time_sum  res_time_min  res_time_max  res_time_avg  \\\n",
       "0  2019162542      8       1057.31         88.75        177.72         132.0   \n",
       "1      162644      5        749.12        103.79        240.38         149.0   \n",
       "2      162742      5        845.84        136.31        225.73         169.0   \n",
       "\n",
       "   interval           created_at  \n",
       "0        60  2018-11-01 00:00:07  \n",
       "1        60  2018-11-01 00:01:07  \n",
       "2        60  2018-11-01 00:02:07  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 179496 entries, 0 to 179495\n",
      "Data columns (total 8 columns):\n",
      " #   Column        Non-Null Count   Dtype  \n",
      "---  ------        --------------   -----  \n",
      " 0   id            179496 non-null  int64  \n",
      " 1   count         179496 non-null  int64  \n",
      " 2   res_time_sum  179496 non-null  float64\n",
      " 3   res_time_min  179496 non-null  float64\n",
      " 4   res_time_max  179496 non-null  float64\n",
      " 5   res_time_avg  179496 non-null  float64\n",
      " 6   interval      179496 non-null  int64  \n",
      " 7   created_at    179496 non-null  object \n",
      "dtypes: float64(4), int64(3), object(1)\n",
      "memory usage: 11.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>interval</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [id, count, res_time_sum, res_time_min, res_time_max, res_time_avg, interval, created_at]\n",
       "Index: []"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.created_at == '2019-05-01']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>interval</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>153089</th>\n",
       "      <td>11406128</td>\n",
       "      <td>6</td>\n",
       "      <td>2105.08</td>\n",
       "      <td>125.74</td>\n",
       "      <td>992.46</td>\n",
       "      <td>350.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 00:00:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153090</th>\n",
       "      <td>11406236</td>\n",
       "      <td>7</td>\n",
       "      <td>2579.11</td>\n",
       "      <td>76.55</td>\n",
       "      <td>987.47</td>\n",
       "      <td>368.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 00:01:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153091</th>\n",
       "      <td>11406347</td>\n",
       "      <td>7</td>\n",
       "      <td>1277.79</td>\n",
       "      <td>109.65</td>\n",
       "      <td>236.73</td>\n",
       "      <td>182.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 00:02:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153092</th>\n",
       "      <td>11406446</td>\n",
       "      <td>7</td>\n",
       "      <td>2137.20</td>\n",
       "      <td>131.55</td>\n",
       "      <td>920.52</td>\n",
       "      <td>305.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 00:03:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153093</th>\n",
       "      <td>11406488</td>\n",
       "      <td>13</td>\n",
       "      <td>2948.70</td>\n",
       "      <td>86.42</td>\n",
       "      <td>491.31</td>\n",
       "      <td>226.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 00:04:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153968</th>\n",
       "      <td>11475363</td>\n",
       "      <td>6</td>\n",
       "      <td>1083.97</td>\n",
       "      <td>70.85</td>\n",
       "      <td>262.22</td>\n",
       "      <td>180.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 23:55:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153969</th>\n",
       "      <td>11475483</td>\n",
       "      <td>4</td>\n",
       "      <td>840.00</td>\n",
       "      <td>117.31</td>\n",
       "      <td>382.63</td>\n",
       "      <td>210.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 23:56:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153970</th>\n",
       "      <td>11475550</td>\n",
       "      <td>2</td>\n",
       "      <td>295.51</td>\n",
       "      <td>101.71</td>\n",
       "      <td>193.80</td>\n",
       "      <td>147.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 23:57:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153971</th>\n",
       "      <td>11475597</td>\n",
       "      <td>2</td>\n",
       "      <td>431.99</td>\n",
       "      <td>84.43</td>\n",
       "      <td>347.56</td>\n",
       "      <td>215.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 23:58:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153972</th>\n",
       "      <td>11475664</td>\n",
       "      <td>3</td>\n",
       "      <td>428.84</td>\n",
       "      <td>103.58</td>\n",
       "      <td>206.57</td>\n",
       "      <td>142.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 23:59:49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>884 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              id  count  res_time_sum  res_time_min  res_time_max  \\\n",
       "153089  11406128      6       2105.08        125.74        992.46   \n",
       "153090  11406236      7       2579.11         76.55        987.47   \n",
       "153091  11406347      7       1277.79        109.65        236.73   \n",
       "153092  11406446      7       2137.20        131.55        920.52   \n",
       "153093  11406488     13       2948.70         86.42        491.31   \n",
       "...          ...    ...           ...           ...           ...   \n",
       "153968  11475363      6       1083.97         70.85        262.22   \n",
       "153969  11475483      4        840.00        117.31        382.63   \n",
       "153970  11475550      2        295.51        101.71        193.80   \n",
       "153971  11475597      2        431.99         84.43        347.56   \n",
       "153972  11475664      3        428.84        103.58        206.57   \n",
       "\n",
       "        res_time_avg  interval           created_at  \n",
       "153089         350.0        60  2019-05-01 00:00:48  \n",
       "153090         368.0        60  2019-05-01 00:01:48  \n",
       "153091         182.0        60  2019-05-01 00:02:48  \n",
       "153092         305.0        60  2019-05-01 00:03:48  \n",
       "153093         226.0        60  2019-05-01 00:04:48  \n",
       "...              ...       ...                  ...  \n",
       "153968         180.0        60  2019-05-01 23:55:49  \n",
       "153969         210.0        60  2019-05-01 23:56:49  \n",
       "153970         147.0        60  2019-05-01 23:57:49  \n",
       "153971         215.0        60  2019-05-01 23:58:49  \n",
       "153972         142.0        60  2019-05-01 23:59:49  \n",
       "\n",
       "[884 rows x 8 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df.created_at >= '2019-05-01') & (df.created_at < '2019-05-02')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=179496, step=1)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.index = df['created_at']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 179496 entries, 2018-11-01 00:00:07 to 2019-05-30 23:10:21\n",
      "Data columns (total 8 columns):\n",
      " #   Column        Non-Null Count   Dtype  \n",
      "---  ------        --------------   -----  \n",
      " 0   id            179496 non-null  int64  \n",
      " 1   count         179496 non-null  int64  \n",
      " 2   res_time_sum  179496 non-null  float64\n",
      " 3   res_time_min  179496 non-null  float64\n",
      " 4   res_time_max  179496 non-null  float64\n",
      " 5   res_time_avg  179496 non-null  float64\n",
      " 6   interval      179496 non-null  int64  \n",
      " 7   created_at    179496 non-null  object \n",
      "dtypes: float64(4), int64(3), object(1)\n",
      "memory usage: 12.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['2018-11-01 00:00:07', '2018-11-01 00:01:07', '2018-11-01 00:02:07',\n",
       "       '2018-11-01 00:03:07', '2018-11-01 00:04:07', '2018-11-01 00:05:07',\n",
       "       '2018-11-01 00:06:07', '2018-11-01 00:07:07', '2018-11-01 00:08:07',\n",
       "       '2018-11-01 00:09:07',\n",
       "       ...\n",
       "       '2019-05-30 23:01:21', '2019-05-30 23:02:21', '2019-05-30 23:03:21',\n",
       "       '2019-05-30 23:04:21', '2019-05-30 23:05:21', '2019-05-30 23:06:21',\n",
       "       '2019-05-30 23:07:21', '2019-05-30 23:08:21', '2019-05-30 23:09:21',\n",
       "       '2019-05-30 23:10:21'],\n",
       "      dtype='object', name='created_at', length=179496)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>interval</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:01:07</th>\n",
       "      <td>162644</td>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:02:07</th>\n",
       "      <td>162742</td>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:02:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:03:07</th>\n",
       "      <td>162808</td>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:03:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:04:07</th>\n",
       "      <td>162943</td>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:04:07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         id  count  res_time_sum  res_time_min  res_time_max  \\\n",
       "created_at                                                                     \n",
       "2018-11-01 00:01:07  162644      5        749.12        103.79        240.38   \n",
       "2018-11-01 00:02:07  162742      5        845.84        136.31        225.73   \n",
       "2018-11-01 00:03:07  162808      9       1305.52         90.12        196.61   \n",
       "2018-11-01 00:04:07  162943      3        568.89        138.45        232.02   \n",
       "\n",
       "                     res_time_avg  interval           created_at  \n",
       "created_at                                                        \n",
       "2018-11-01 00:01:07         149.0        60  2018-11-01 00:01:07  \n",
       "2018-11-01 00:02:07         169.0        60  2018-11-01 00:02:07  \n",
       "2018-11-01 00:03:07         145.0        60  2018-11-01 00:03:07  \n",
       "2018-11-01 00:04:07         189.0        60  2018-11-01 00:04:07  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[1:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.index = pd.to_datetime(df.created_at)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2018-11-01 00:00:07', '2018-11-01 00:01:07',\n",
       "               '2018-11-01 00:02:07', '2018-11-01 00:03:07',\n",
       "               '2018-11-01 00:04:07', '2018-11-01 00:05:07',\n",
       "               '2018-11-01 00:06:07', '2018-11-01 00:07:07',\n",
       "               '2018-11-01 00:08:07', '2018-11-01 00:09:07',\n",
       "               ...\n",
       "               '2019-05-30 23:01:21', '2019-05-30 23:02:21',\n",
       "               '2019-05-30 23:03:21', '2019-05-30 23:04:21',\n",
       "               '2019-05-30 23:05:21', '2019-05-30 23:06:21',\n",
       "               '2019-05-30 23:07:21', '2019-05-30 23:08:21',\n",
       "               '2019-05-30 23:09:21', '2019-05-30 23:10:21'],\n",
       "              dtype='datetime64[ns]', name='created_at', length=179496, freq=None)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>created_at</th>\n",
       "      <th>weekday</th>\n",
       "      <th>weekend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-05-01 00:00:48</th>\n",
       "      <td>6</td>\n",
       "      <td>2105.08</td>\n",
       "      <td>125.74</td>\n",
       "      <td>992.46</td>\n",
       "      <td>350.0</td>\n",
       "      <td>2019-05-01 00:00:48</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 00:01:48</th>\n",
       "      <td>7</td>\n",
       "      <td>2579.11</td>\n",
       "      <td>76.55</td>\n",
       "      <td>987.47</td>\n",
       "      <td>368.0</td>\n",
       "      <td>2019-05-01 00:01:48</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 00:02:48</th>\n",
       "      <td>7</td>\n",
       "      <td>1277.79</td>\n",
       "      <td>109.65</td>\n",
       "      <td>236.73</td>\n",
       "      <td>182.0</td>\n",
       "      <td>2019-05-01 00:02:48</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 00:03:48</th>\n",
       "      <td>7</td>\n",
       "      <td>2137.20</td>\n",
       "      <td>131.55</td>\n",
       "      <td>920.52</td>\n",
       "      <td>305.0</td>\n",
       "      <td>2019-05-01 00:03:48</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 00:04:48</th>\n",
       "      <td>13</td>\n",
       "      <td>2948.70</td>\n",
       "      <td>86.42</td>\n",
       "      <td>491.31</td>\n",
       "      <td>226.0</td>\n",
       "      <td>2019-05-01 00:04:48</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 23:55:49</th>\n",
       "      <td>6</td>\n",
       "      <td>1083.97</td>\n",
       "      <td>70.85</td>\n",
       "      <td>262.22</td>\n",
       "      <td>180.0</td>\n",
       "      <td>2019-05-01 23:55:49</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 23:56:49</th>\n",
       "      <td>4</td>\n",
       "      <td>840.00</td>\n",
       "      <td>117.31</td>\n",
       "      <td>382.63</td>\n",
       "      <td>210.0</td>\n",
       "      <td>2019-05-01 23:56:49</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 23:57:49</th>\n",
       "      <td>2</td>\n",
       "      <td>295.51</td>\n",
       "      <td>101.71</td>\n",
       "      <td>193.80</td>\n",
       "      <td>147.0</td>\n",
       "      <td>2019-05-01 23:57:49</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 23:58:49</th>\n",
       "      <td>2</td>\n",
       "      <td>431.99</td>\n",
       "      <td>84.43</td>\n",
       "      <td>347.56</td>\n",
       "      <td>215.0</td>\n",
       "      <td>2019-05-01 23:58:49</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 23:59:49</th>\n",
       "      <td>3</td>\n",
       "      <td>428.84</td>\n",
       "      <td>103.58</td>\n",
       "      <td>206.57</td>\n",
       "      <td>142.0</td>\n",
       "      <td>2019-05-01 23:59:49</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>884 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count  res_time_sum  res_time_min  res_time_max  \\\n",
       "created_at                                                             \n",
       "2019-05-01 00:00:48      6       2105.08        125.74        992.46   \n",
       "2019-05-01 00:01:48      7       2579.11         76.55        987.47   \n",
       "2019-05-01 00:02:48      7       1277.79        109.65        236.73   \n",
       "2019-05-01 00:03:48      7       2137.20        131.55        920.52   \n",
       "2019-05-01 00:04:48     13       2948.70         86.42        491.31   \n",
       "...                    ...           ...           ...           ...   \n",
       "2019-05-01 23:55:49      6       1083.97         70.85        262.22   \n",
       "2019-05-01 23:56:49      4        840.00        117.31        382.63   \n",
       "2019-05-01 23:57:49      2        295.51        101.71        193.80   \n",
       "2019-05-01 23:58:49      2        431.99         84.43        347.56   \n",
       "2019-05-01 23:59:49      3        428.84        103.58        206.57   \n",
       "\n",
       "                     res_time_avg           created_at  weekday  weekend  \n",
       "created_at                                                                \n",
       "2019-05-01 00:00:48         350.0  2019-05-01 00:00:48        2    False  \n",
       "2019-05-01 00:01:48         368.0  2019-05-01 00:01:48        2    False  \n",
       "2019-05-01 00:02:48         182.0  2019-05-01 00:02:48        2    False  \n",
       "2019-05-01 00:03:48         305.0  2019-05-01 00:03:48        2    False  \n",
       "2019-05-01 00:04:48         226.0  2019-05-01 00:04:48        2    False  \n",
       "...                           ...                  ...      ...      ...  \n",
       "2019-05-01 23:55:49         180.0  2019-05-01 23:55:49        2    False  \n",
       "2019-05-01 23:56:49         210.0  2019-05-01 23:56:49        2    False  \n",
       "2019-05-01 23:57:49         147.0  2019-05-01 23:57:49        2    False  \n",
       "2019-05-01 23:58:49         215.0  2019-05-01 23:58:49        2    False  \n",
       "2019-05-01 23:59:49         142.0  2019-05-01 23:59:49        2    False  \n",
       "\n",
       "[884 rows x 8 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['2019-05-01']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    179496.0\n",
       "mean         60.0\n",
       "std           0.0\n",
       "min          60.0\n",
       "25%          60.0\n",
       "50%          60.0\n",
       "75%          60.0\n",
       "max          60.0\n",
       "Name: interval, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.interval.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>interval</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:00:07</th>\n",
       "      <td>2019162542</td>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:01:07</th>\n",
       "      <td>162644</td>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:02:07</th>\n",
       "      <td>162742</td>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:02:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:03:07</th>\n",
       "      <td>162808</td>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:03:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:04:07</th>\n",
       "      <td>162943</td>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:04:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 23:06:21</th>\n",
       "      <td>13438800</td>\n",
       "      <td>11</td>\n",
       "      <td>2783.48</td>\n",
       "      <td>99.24</td>\n",
       "      <td>489.90</td>\n",
       "      <td>253.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 23:06:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 23:07:21</th>\n",
       "      <td>13438866</td>\n",
       "      <td>10</td>\n",
       "      <td>1951.10</td>\n",
       "      <td>85.37</td>\n",
       "      <td>529.51</td>\n",
       "      <td>195.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 23:07:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 23:08:21</th>\n",
       "      <td>13438917</td>\n",
       "      <td>3</td>\n",
       "      <td>494.17</td>\n",
       "      <td>103.95</td>\n",
       "      <td>211.47</td>\n",
       "      <td>164.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 23:08:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 23:09:21</th>\n",
       "      <td>13438981</td>\n",
       "      <td>9</td>\n",
       "      <td>1798.28</td>\n",
       "      <td>101.11</td>\n",
       "      <td>433.30</td>\n",
       "      <td>199.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 23:09:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 23:10:21</th>\n",
       "      <td>13439086</td>\n",
       "      <td>6</td>\n",
       "      <td>1017.97</td>\n",
       "      <td>74.45</td>\n",
       "      <td>298.97</td>\n",
       "      <td>169.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 23:10:21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>179496 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             id  count  res_time_sum  res_time_min  \\\n",
       "created_at                                                           \n",
       "2018-11-01 00:00:07  2019162542      8       1057.31         88.75   \n",
       "2018-11-01 00:01:07      162644      5        749.12        103.79   \n",
       "2018-11-01 00:02:07      162742      5        845.84        136.31   \n",
       "2018-11-01 00:03:07      162808      9       1305.52         90.12   \n",
       "2018-11-01 00:04:07      162943      3        568.89        138.45   \n",
       "...                         ...    ...           ...           ...   \n",
       "2019-05-30 23:06:21    13438800     11       2783.48         99.24   \n",
       "2019-05-30 23:07:21    13438866     10       1951.10         85.37   \n",
       "2019-05-30 23:08:21    13438917      3        494.17        103.95   \n",
       "2019-05-30 23:09:21    13438981      9       1798.28        101.11   \n",
       "2019-05-30 23:10:21    13439086      6       1017.97         74.45   \n",
       "\n",
       "                     res_time_max  res_time_avg  interval           created_at  \n",
       "created_at                                                                      \n",
       "2018-11-01 00:00:07        177.72         132.0        60  2018-11-01 00:00:07  \n",
       "2018-11-01 00:01:07        240.38         149.0        60  2018-11-01 00:01:07  \n",
       "2018-11-01 00:02:07        225.73         169.0        60  2018-11-01 00:02:07  \n",
       "2018-11-01 00:03:07        196.61         145.0        60  2018-11-01 00:03:07  \n",
       "2018-11-01 00:04:07        232.02         189.0        60  2018-11-01 00:04:07  \n",
       "...                           ...           ...       ...                  ...  \n",
       "2019-05-30 23:06:21        489.90         253.0        60  2019-05-30 23:06:21  \n",
       "2019-05-30 23:07:21        529.51         195.0        60  2019-05-30 23:07:21  \n",
       "2019-05-30 23:08:21        211.47         164.0        60  2019-05-30 23:08:21  \n",
       "2019-05-30 23:09:21        433.30         199.0        60  2019-05-30 23:09:21  \n",
       "2019-05-30 23:10:21        298.97         169.0        60  2019-05-30 23:10:21  \n",
       "\n",
       "[179496 rows x 8 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:00:07</th>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:01:07</th>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:02:07</th>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>2018-11-01 00:02:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:03:07</th>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>2018-11-01 00:03:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:04:07</th>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>2018-11-01 00:04:07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count  res_time_sum  res_time_min  res_time_max  \\\n",
       "created_at                                                             \n",
       "2018-11-01 00:00:07      8       1057.31         88.75        177.72   \n",
       "2018-11-01 00:01:07      5        749.12        103.79        240.38   \n",
       "2018-11-01 00:02:07      5        845.84        136.31        225.73   \n",
       "2018-11-01 00:03:07      9       1305.52         90.12        196.61   \n",
       "2018-11-01 00:04:07      3        568.89        138.45        232.02   \n",
       "\n",
       "                     res_time_avg           created_at  \n",
       "created_at                                              \n",
       "2018-11-01 00:00:07         132.0  2018-11-01 00:00:07  \n",
       "2018-11-01 00:01:07         149.0  2018-11-01 00:01:07  \n",
       "2018-11-01 00:02:07         169.0  2018-11-01 00:02:07  \n",
       "2018-11-01 00:03:07         145.0  2018-11-01 00:03:07  \n",
       "2018-11-01 00:04:07         189.0  2018-11-01 00:04:07  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 删除无用数据列\n",
    "df = df.drop(['id', 'interval'], axis = 1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 179496 entries, 2018-11-01 00:00:07 to 2019-05-30 23:10:21\n",
      "Data columns (total 6 columns):\n",
      " #   Column        Non-Null Count   Dtype  \n",
      "---  ------        --------------   -----  \n",
      " 0   count         179496 non-null  int64  \n",
      " 1   res_time_sum  179496 non-null  float64\n",
      " 2   res_time_min  179496 non-null  float64\n",
      " 3   res_time_max  179496 non-null  float64\n",
      " 4   res_time_avg  179496 non-null  float64\n",
      " 5   created_at    179496 non-null  object \n",
      "dtypes: float64(4), int64(1), object(1)\n",
      "memory usage: 9.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.175909</td>\n",
       "      <td>1393.177832</td>\n",
       "      <td>108.419626</td>\n",
       "      <td>359.880374</td>\n",
       "      <td>187.812208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.325160</td>\n",
       "      <td>1499.486073</td>\n",
       "      <td>79.640693</td>\n",
       "      <td>638.919827</td>\n",
       "      <td>224.464813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>36.550000</td>\n",
       "      <td>3.210000</td>\n",
       "      <td>36.550000</td>\n",
       "      <td>36.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>607.707500</td>\n",
       "      <td>83.410000</td>\n",
       "      <td>198.280000</td>\n",
       "      <td>144.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>1154.905000</td>\n",
       "      <td>97.120000</td>\n",
       "      <td>256.090000</td>\n",
       "      <td>167.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>1834.117500</td>\n",
       "      <td>116.990000</td>\n",
       "      <td>374.410000</td>\n",
       "      <td>202.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>31.000000</td>\n",
       "      <td>142650.550000</td>\n",
       "      <td>18896.640000</td>\n",
       "      <td>142468.270000</td>\n",
       "      <td>71325.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               count   res_time_sum   res_time_min   res_time_max  \\\n",
       "count  179496.000000  179496.000000  179496.000000  179496.000000   \n",
       "mean        7.175909    1393.177832     108.419626     359.880374   \n",
       "std         4.325160    1499.486073      79.640693     638.919827   \n",
       "min         1.000000      36.550000       3.210000      36.550000   \n",
       "25%         4.000000     607.707500      83.410000     198.280000   \n",
       "50%         7.000000    1154.905000      97.120000     256.090000   \n",
       "75%        10.000000    1834.117500     116.990000     374.410000   \n",
       "max        31.000000  142650.550000   18896.640000  142468.270000   \n",
       "\n",
       "        res_time_avg  \n",
       "count  179496.000000  \n",
       "mean      187.812208  \n",
       "std       224.464813  \n",
       "min        36.000000  \n",
       "25%       144.000000  \n",
       "50%       167.000000  \n",
       "75%       202.000000  \n",
       "max     71325.000000  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['count'].hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['count'].hist(bins = 30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.loc['2019-05-01']['count'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df.loc['2019-5-1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-05-01 00:00:00</th>\n",
       "      <td>4.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 01:00:00</th>\n",
       "      <td>2.272727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 02:00:00</th>\n",
       "      <td>1.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 03:00:00</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 04:00:00</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 05:00:00</th>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 06:00:00</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 07:00:00</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 08:00:00</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 09:00:00</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 10:00:00</th>\n",
       "      <td>1.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 11:00:00</th>\n",
       "      <td>1.604651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 12:00:00</th>\n",
       "      <td>3.298246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 13:00:00</th>\n",
       "      <td>6.866667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 14:00:00</th>\n",
       "      <td>10.483333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 15:00:00</th>\n",
       "      <td>12.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 16:00:00</th>\n",
       "      <td>9.916667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 17:00:00</th>\n",
       "      <td>7.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 18:00:00</th>\n",
       "      <td>6.783333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 19:00:00</th>\n",
       "      <td>9.850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 20:00:00</th>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 21:00:00</th>\n",
       "      <td>10.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 22:00:00</th>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 23:00:00</th>\n",
       "      <td>5.083333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         count\n",
       "created_at                    \n",
       "2019-05-01 00:00:00   4.428571\n",
       "2019-05-01 01:00:00   2.272727\n",
       "2019-05-01 02:00:00   1.833333\n",
       "2019-05-01 03:00:00        NaN\n",
       "2019-05-01 04:00:00        NaN\n",
       "2019-05-01 05:00:00   2.000000\n",
       "2019-05-01 06:00:00        NaN\n",
       "2019-05-01 07:00:00        NaN\n",
       "2019-05-01 08:00:00        NaN\n",
       "2019-05-01 09:00:00   1.000000\n",
       "2019-05-01 10:00:00   1.400000\n",
       "2019-05-01 11:00:00   1.604651\n",
       "2019-05-01 12:00:00   3.298246\n",
       "2019-05-01 13:00:00   6.866667\n",
       "2019-05-01 14:00:00  10.483333\n",
       "2019-05-01 15:00:00  12.333333\n",
       "2019-05-01 16:00:00   9.916667\n",
       "2019-05-01 17:00:00   7.666667\n",
       "2019-05-01 18:00:00   6.783333\n",
       "2019-05-01 19:00:00   9.850000\n",
       "2019-05-01 20:00:00  11.000000\n",
       "2019-05-01 21:00:00  10.416667\n",
       "2019-05-01 22:00:00   8.000000\n",
       "2019-05-01 23:00:00   5.083333"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df2[['count']].resample('1H').mean()\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2['count'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10, 3))\n",
    "df2['count'].plot(kind = 'bar')\n",
    "plt.xticks(rotation = 60)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.loc['2019-5-1'][['count']].boxplot(showmeans=True, meanline=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-01 20:47:09</th>\n",
       "      <td>21</td>\n",
       "      <td>3117.20</td>\n",
       "      <td>84.90</td>\n",
       "      <td>260.82</td>\n",
       "      <td>148.0</td>\n",
       "      <td>2018-11-01 20:47:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 21:03:09</th>\n",
       "      <td>21</td>\n",
       "      <td>3706.20</td>\n",
       "      <td>78.12</td>\n",
       "      <td>321.47</td>\n",
       "      <td>176.0</td>\n",
       "      <td>2018-11-01 21:03:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 21:13:09</th>\n",
       "      <td>24</td>\n",
       "      <td>4602.03</td>\n",
       "      <td>76.31</td>\n",
       "      <td>391.12</td>\n",
       "      <td>191.0</td>\n",
       "      <td>2018-11-01 21:13:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-02 21:34:11</th>\n",
       "      <td>30</td>\n",
       "      <td>4610.15</td>\n",
       "      <td>72.49</td>\n",
       "      <td>463.41</td>\n",
       "      <td>153.0</td>\n",
       "      <td>2018-11-02 21:34:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-03 14:20:13</th>\n",
       "      <td>21</td>\n",
       "      <td>3113.93</td>\n",
       "      <td>74.29</td>\n",
       "      <td>266.20</td>\n",
       "      <td>148.0</td>\n",
       "      <td>2018-11-03 14:20:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 21:33:21</th>\n",
       "      <td>27</td>\n",
       "      <td>6456.64</td>\n",
       "      <td>99.65</td>\n",
       "      <td>978.91</td>\n",
       "      <td>239.0</td>\n",
       "      <td>2019-05-30 21:33:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 21:43:21</th>\n",
       "      <td>21</td>\n",
       "      <td>6371.84</td>\n",
       "      <td>65.98</td>\n",
       "      <td>1175.37</td>\n",
       "      <td>303.0</td>\n",
       "      <td>2019-05-30 21:43:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 21:47:21</th>\n",
       "      <td>21</td>\n",
       "      <td>3992.83</td>\n",
       "      <td>87.83</td>\n",
       "      <td>440.88</td>\n",
       "      <td>190.0</td>\n",
       "      <td>2019-05-30 21:47:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 21:53:21</th>\n",
       "      <td>24</td>\n",
       "      <td>8467.02</td>\n",
       "      <td>120.22</td>\n",
       "      <td>1511.17</td>\n",
       "      <td>352.0</td>\n",
       "      <td>2019-05-30 21:53:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 22:17:21</th>\n",
       "      <td>21</td>\n",
       "      <td>4926.35</td>\n",
       "      <td>85.01</td>\n",
       "      <td>826.90</td>\n",
       "      <td>234.0</td>\n",
       "      <td>2019-05-30 22:17:21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>746 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count  res_time_sum  res_time_min  res_time_max  \\\n",
       "created_at                                                             \n",
       "2018-11-01 20:47:09     21       3117.20         84.90        260.82   \n",
       "2018-11-01 21:03:09     21       3706.20         78.12        321.47   \n",
       "2018-11-01 21:13:09     24       4602.03         76.31        391.12   \n",
       "2018-11-02 21:34:11     30       4610.15         72.49        463.41   \n",
       "2018-11-03 14:20:13     21       3113.93         74.29        266.20   \n",
       "...                    ...           ...           ...           ...   \n",
       "2019-05-30 21:33:21     27       6456.64         99.65        978.91   \n",
       "2019-05-30 21:43:21     21       6371.84         65.98       1175.37   \n",
       "2019-05-30 21:47:21     21       3992.83         87.83        440.88   \n",
       "2019-05-30 21:53:21     24       8467.02        120.22       1511.17   \n",
       "2019-05-30 22:17:21     21       4926.35         85.01        826.90   \n",
       "\n",
       "                     res_time_avg           created_at  \n",
       "created_at                                              \n",
       "2018-11-01 20:47:09         148.0  2018-11-01 20:47:09  \n",
       "2018-11-01 21:03:09         176.0  2018-11-01 21:03:09  \n",
       "2018-11-01 21:13:09         191.0  2018-11-01 21:13:09  \n",
       "2018-11-02 21:34:11         153.0  2018-11-02 21:34:11  \n",
       "2018-11-03 14:20:13         148.0  2018-11-03 14:20:13  \n",
       "...                           ...                  ...  \n",
       "2019-05-30 21:33:21         239.0  2019-05-30 21:33:21  \n",
       "2019-05-30 21:43:21         303.0  2019-05-30 21:43:21  \n",
       "2019-05-30 21:47:21         190.0  2019-05-30 21:47:21  \n",
       "2019-05-30 21:53:21         352.0  2019-05-30 21:53:21  \n",
       "2019-05-30 22:17:21         234.0  2019-05-30 22:17:21  \n",
       "\n",
       "[746 rows x 6 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['count'] > 20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.loc['2019-5-1']['res_time_avg'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.loc['2019-5-1'][['res_time_avg']].boxplot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>created_at</th>\n",
       "      <th>weekday</th>\n",
       "      <th>weekend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-05-01 00:34:48</th>\n",
       "      <td>1</td>\n",
       "      <td>1694.47</td>\n",
       "      <td>1694.47</td>\n",
       "      <td>1694.47</td>\n",
       "      <td>1694.0</td>\n",
       "      <td>2019-05-01 00:34:48</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 14:00:49</th>\n",
       "      <td>17</td>\n",
       "      <td>19770.18</td>\n",
       "      <td>207.54</td>\n",
       "      <td>2974.52</td>\n",
       "      <td>1162.0</td>\n",
       "      <td>2019-05-01 14:00:49</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 18:36:49</th>\n",
       "      <td>8</td>\n",
       "      <td>8799.92</td>\n",
       "      <td>96.59</td>\n",
       "      <td>3233.26</td>\n",
       "      <td>1099.0</td>\n",
       "      <td>2019-05-01 18:36:49</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 19:09:49</th>\n",
       "      <td>6</td>\n",
       "      <td>7399.94</td>\n",
       "      <td>307.39</td>\n",
       "      <td>3153.02</td>\n",
       "      <td>1233.0</td>\n",
       "      <td>2019-05-01 19:09:49</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 19:10:49</th>\n",
       "      <td>13</td>\n",
       "      <td>23595.60</td>\n",
       "      <td>206.20</td>\n",
       "      <td>4664.84</td>\n",
       "      <td>1815.0</td>\n",
       "      <td>2019-05-01 19:10:49</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 20:38:49</th>\n",
       "      <td>15</td>\n",
       "      <td>16169.25</td>\n",
       "      <td>142.47</td>\n",
       "      <td>3624.26</td>\n",
       "      <td>1077.0</td>\n",
       "      <td>2019-05-01 20:38:49</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count  res_time_sum  res_time_min  res_time_max  \\\n",
       "created_at                                                             \n",
       "2019-05-01 00:34:48      1       1694.47       1694.47       1694.47   \n",
       "2019-05-01 14:00:49     17      19770.18        207.54       2974.52   \n",
       "2019-05-01 18:36:49      8       8799.92         96.59       3233.26   \n",
       "2019-05-01 19:09:49      6       7399.94        307.39       3153.02   \n",
       "2019-05-01 19:10:49     13      23595.60        206.20       4664.84   \n",
       "2019-05-01 20:38:49     15      16169.25        142.47       3624.26   \n",
       "\n",
       "                     res_time_avg           created_at  weekday  weekend  \n",
       "created_at                                                                \n",
       "2019-05-01 00:34:48        1694.0  2019-05-01 00:34:48        2    False  \n",
       "2019-05-01 14:00:49        1162.0  2019-05-01 14:00:49        2    False  \n",
       "2019-05-01 18:36:49        1099.0  2019-05-01 18:36:49        2    False  \n",
       "2019-05-01 19:09:49        1233.0  2019-05-01 19:09:49        2    False  \n",
       "2019-05-01 19:10:49        1815.0  2019-05-01 19:10:49        2    False  \n",
       "2019-05-01 20:38:49        1077.0  2019-05-01 20:38:49        2    False  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df.loc['2019-5-1']\n",
    "df2[df2['res_time_avg'] > 1000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.loc['2019-5-1'][['res_time_sum', 'res_time_min', 'res_time_max', 'res_time_avg']].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x360 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "data = df.loc['2019-5-1'].resample('20T').mean()\n",
    "data[['res_time_sum', 'res_time_min', 'res_time_max', 'res_time_avg']].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAENCAYAAADgwHn9AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAA/MUlEQVR4nO2deZgcVdX/v2dmskIIgcTIakCU7QWDRARRdpRVwPcVH1QEQVF/KKgob0RBFFRUEAUBCS9IUGQHWcJiErIQyMIkZJvsy2SZbJNlkklmnzm/P6q6p6anqvt0962qW93n8zzzTHf1rapv3aq65y7nnkvMDEVRFEUBgIq4BSiKoij2oEZBURRFSaNGQVEURUmjRkFRFEVJo0ZBURRFSVMVt4BiGDp0KI8YMSJuGYqiKIli9uzZW5l5mN9viTYKI0aMQHV1ddwyFEVREgURrQn6TbuPFEVRlDRqFBRFUZQ0ahQURVGUNGoUFEVRlDRqFBRFUZQ0ahQURVGUNGoUFMVS2ju78MaCjdBIxkqUqFFQFEu5b+JyfO/JOZi0dEvcUpQyQo2ColhKXUMzAGD7nvaYlSjlhBoFRVEUJU3kRoGI+hPRLCKaR0Q1RPQrd/thRDSTiFYQ0TNE1DdqbYqiKOVOHC2FVgBnMfMnAIwEcB4RnQzg9wDuZeYjAOwAcG0M2hRFUcqayI0CO+x2v/Zx/xjAWQCed7ePBXBp1NqSwhsLNmLLrpa4ZSiKUoLEMqZARJVENBfAFgDjAawE0MDMHW6S9QAOCtj3OiKqJqLq+vr6SPTaRFNbB7735Bx8/dGZcUtRFKUEicUoMHMnM48EcDCAkwAclce+Y5h5FDOPGjbMNxx4SdPZ5fisb2jQloKiKOaJ1fuImRsATAJwCoB9iSi1vsPBAOri0qUoilKuxOF9NIyI9nU/DwBwLoDFcIzD/7jJrgLwctTaFEVRyp04Vl47AMBYIqqEY5SeZebXiGgRgKeJ6E4AHwB4NAZtiqIoZU3kRoGZ5wM4wWf7KjjjC2XP1GX1GNi3EqNG7Be3FMUCNPaREiWJXqO5VPnGY7MAALV3XRizEiVOCBS3BKUM0TAXiqIoSho1CglDOxIURQkTNQoJRTsWFEUJAzUKiqIoSho1CopSJkxeugVv1WyKW0ZJ8M8Za1CzYWfcMkJBvY8UpUy4+u/vA1CvNhP84t8LAZRmXmpLQVEURUmjRkFRFEVJo0YhYejkVkVRwkSNQlJRn1RFUUJAjULSSFBLobOLcfsrNdi4szluKYlGest3Nrfj5y8tQEt7Z6h6MmFm/OHNJVi+uTHS84ZBU1sHfv7SAjS2tPv+vrBuJ/4yYXnEqnry6rwNeHlueCsLqFFIKEloKMxctQ2Pv1eLnz43P24piYTyvMn3TVyOJ2euxVOz1oYjKIDte9rw4OSVuOKR5K8G+I/pa/DkzLV4YNJK398vun8a7p2wLGJVPfnBUx/gxqfnhnZ8NQpKaKRquF06EBIJqVX5os7u1OlKIZqrm4XgJDXJDaNGQQmNEigjFKXsUKOghE6+3SCKosSHGgVFUYpCW4SlhRoFy3jm/eyDhEnq65xf1wBAVmj83zurcl57Piys24mbnp2Hrq7k5FfSKYUW4fRV2/JK39oRradXfWNr6OdQo2AZ//vCAlE6SsAb+Ic3lwIAtu1uy5n2znGLxdcu4ZrH38cLc9ajfnf4L5FSOkxdVp9X+vdW5mdEiuWvb4fvDqtGQVEURUmjRkFRlKJIUpemkpvIjQIRHUJEk4hoERHVENGN7vbbiaiOiOa6fxdErU0JhwT0dCnFkLYJeqNLgTjWU+gAcBMzzyGiQQBmE9F497d7mfnuGDQpir0kpCKuxr80iLylwMwbmXmO+7kRwGIAB0WtI6lscb0Pdjb7x2YpZeavb8Alf50miu1juhx9cc56XP+vOUUdo7WjE1968F1U124XpQ+rjB2/aDO+8disnOnGvleL0S/YF6JkYd1OXHz/NDS1dcQtBdOWb430fFE4mMQ6pkBEIwCcACAVNOX7RDSfiB4joiEB+1xHRNVEVF1fn5+nQCnwXPW6uCXExq9fXYR563diQZ18GURTr9CPn52HcfM3FnWM1Vv3YM7aBvz8pYWGVBXGt5+oFnnZ/PKVGjz9vn3P229fX4wFdTvxwdqGuKXg0Wmr45ZgnNiMAhHtDeAFAD9k5l0AHgLwUQAjAWwEcI/ffsw8hplHMfOoYcOGRSXXGnSikKKED5Xx+EgsRoGI+sAxCE8y84sAwMybmbmTmbsAPALgpDi0KYotJMX+J0WnIiMO7yMC8CiAxcz8J8/2AzzJLgMQbxtbsZayay0lpNIalcwo7n85u9nG4X10KoArASwgornutlsAXEFEI+FUPGoBfCcGbdZTvo9qt3eLJERz2RmOGIkrrxNiKxNHHN5H05iZmPl4Zh7p/r3OzFcy83Hu9i8yc3GjegWwY08bjr/9LcxZu0O8z/Oz1+PMuyeHJyqDUirsvv1ENX73xmJx+k27WgAAXxkzI2u6OWt3YGsqvIWWHHnR0t6Jk34zARMWbc573zhcUn/87Fz87MXiw6O8s7weJ94xPndCISu27Maxt72J9TuaxPuMunM8Rowel3Wf6RGE1dAZzR7er92OXS0deDBg1SU/fvLcPKzeuidEVaXL+EWb8fCUVeL067bLlvV8cNKKQiWVBMXUGzbubMGWxlbcOW6RMT1hwQBenFNnZKW537+5BNv25I7RJeXpWWuxp60TbyzYJN5nqxsj7M2FwfssjWDJUzUKSugkIXifUjil2P+u3keKopQM0uLM9PKZURek5Vtsh4sahYRRirUyRVHsQY2CEjqlsKB7rGj2KRGiRiEi1u9owojR4zB7jSzuTRCTl8pCe+xu7cCI0eMwbv5GjBg9DiNGj8MHeXhVZeP2V2pwlkGPqysfnZk7UYEEdWn890Pv4YanPsAT02sxYvQ4Yyu0efO9WFLKb35hPm5/pabo4+VD7Ta518xZd08JUUlvCmktf+OxWbj28fcDf19Yt6vH99fmb8j7HH5MW7EVI0aPC1wxbcWWRowYPQ6LNuzy/T0O1ChExLsrnMBZzxQZS0bq6bTWfanv96zU9MT0NUWdO8Xj79VilUGPq3ciDioGALPX7MAr8zbgztccl9j2ri4jx/XLdxM8/l6t0eMB/u7NhfTTN7sBCm32J5i6rB4Tl2wRp1+/Q+bplospboypeesafH9/q8Zx/TVlhEygRsEXba+bRL2PyoOo7nISPIOS/MirUYgI7VaPEvkbqQP3im3EXYlSo+BLeDclqlqOX2EX14BvEipNSah9Sil1M1dKhtzvnYz7SVSjEBFxPcbeWkfpvErZSXLTvRjK7rpL6HptundqFCImn5u/Pce0+5RXkSkemrwSI0aPQ0enbND1N8JQCGGsErezKfiYs9d0e1n55fevXu325GnvdEzlnycsM6LrxTnrAQBLNmUPR7ChoRkjRo/Df2r8QxrMXdeA52av77Ftsxv7yY89rR34+7u1gb93+nhXSRfQMeFJlfLK+ucMM84OAHBNFm+iMPjLhMKdB771RHXWlvrExfJB8LBRoxARhfTc1G4r3MOnkPOlPGZaO2RG4ZF3ZKtO1TWY8eTwsr4h2GUylzH1KzwfnCyPd5WNsdN7HzsTBqdXj8ss+FOMX9TbWGQzNFs8Lo9+9Y52H0P/2Lu975+fEZXGFsrWF75pZ0vgOQulpd2Mx5iUP080U3HwknpPvfc27laDGoUSx/t86WB3vJTSuIWSnbgHi4tBjYIv5kvPQp4R049VrqtSo6EoihoFD2Fa9yQVuAmu5CQGG54Hvz5uG3Qp8RLHymvW4n1JOrsYFWTeUMRZ4JZSDKKgrpjMcBVJtW9+UTeC7h8zoyvHvfX7OaqnwbknnD4pM/d6r5g5rbGigtL3saKi9x2UPsbeY5qAYD7P/I4X9zOrRsGHxRsb8dFbXseQgX3wwW2fN3LMZ6odT4+NO4M9SDIxYZAWbeyOqZKz+8gCp9Xmtk4M6FvZa/uz1T09ZarXbMcxB+7TK93ht7wemjYJlGfRMT5ghbOH8hj4vm/iCtybw3vqnv8sFR2rmEcuyKHg8Ftex8mH7wcAWLV1D8750xRMvOmMHml+98YSjJnqLLj0j2tPwpWPzgIA1N51YcF6Lrp/GmoEMYWyLWrjhYgiaUrV7/aPk5RJY0s7BvXvY/z82n3kQ+rh3pHF7TFfUrFPolqlrZhnN84B0cYW/zx/ZW7P2DCpWFK2EYdh/desnm6efgqe8HMFjVDqjFXdgSBX1vd+B7yxnSYtkQV9zIXEIADAWwFuwaaRvpPSuEs79ph39QbUKFhN3M1IJRmUUK+giLi6YMM4rY33LnKjQESHENEkIlpERDVEdKO7fT8iGk9Ey93/Q6LWVvJY+ACWGvJWVv43Q2+fg40FaSbyp0DDXABAB4CbmPkYACcDuJ6IjgEwGsBEZv4YgInu97LGdI0oV9dGEl62UiHMvI67UClVbPPKC6urMnKjwMwbmXmO+7kRwGIABwG4BMBYN9lYAJdGrS0M2ju70NTWEfl5CzlnaiZzsQ+/6bAWzW2d2JZjljIAtLhx/b0UO1jf2tHpe9xABKdrzud4OWhp7xTNQPeTpXUAh1yeWyninnwYRrgYP2L1PiKiEQBOADATwHBmTgVZ2QRgeAx6jB/z2rHVmLrMzMBZPnxlzIyC911Vv8fXs0fCWzWb8J1/zMYL3zul4PNncvRtb4rSHXWrLF0+fOrOCdjV0m1gNzQ048B9BxR1zHXbm7G9KbeRy6S5rbcx8btmaZgSPxfXN3w8cbZm8YbJy2BmIc7i9uW5sgVupMYDkBtc34WOfMqhWau34/KHp/fYNnddAz6y/15iTVJiG2gmor0BvADgh8zcw02AnafVN1+J6Doiqiai6vp6s4VtGH78xRgE0zUT6eWt2rq74HNMX7kNADBv3c6Cj2ETXoMAOEbBBFsb8zcKe1plrb/WjsILaj+vri0BS0kCQJOPoSqEfOpjcbVwojqvXzk0d13vpXQXrA/nHYvFKBBRHzgG4UlmftHdvJmIDnB/PwCAb9hAZh7DzKOYedSwYcOiEWyQOGtEUqMQazNZeOq4m/JxEMXEKSU8pPkdd9ykOLyPCMCjABYz8588P70C4Cr381UAXo5am23YNrBlEzZMtCuGMO9tqTsMxPVa5HPeYnzQwvNfkxHHmMKpAK4EsICI5rrbbgFwF4BniehaAGsAXB6DttCJuxYgIQESxUR9KWGer6iCxmdnW2+zrc9fZLpivv7IjQIzT0PwZZ8dpRYJQVPJ6xtbMah//tkXNKN5T2tH1gE9E0hr10E3p6GpDVWV2RuXHV1d7rmUbNhQmzchoU44+zYX+XQHxraKYQjRj1bVFz5+FxY6ozkHx93+H9/tn/rNhB4rP81Z23sgKAg/d9Ez756M0/84OW99ALBxZ88Xc1dAqAi/IGv5MPLX43HKbydmTfPPGc6CLA9NXlHQOSYLQxxIB9nWbg9ejCcXQflogr9NyX9RH/Ht8133t7jAcn68PLcOF/91mlRVVrxuun6rxHnxW3VvW8gVKgBoE65ImA+vCVe1MzWgL0GNgod8u3becz1tAGDFFrnF93Mt9PPwkMrZtrunJ8vuFn8vFenVZTtvo9ADZuvu/L1rAKBmg6yw3yAMLJhPAMJMgvLRBCbnKsTFnDXyilA+5PIC9DPWJuOU2YhfmRFWa1ONQhkhf4Ys7dQtiGg7G8I8WyndlXLEtMt7ycxotpliblqcL6y0RSF2SdXSp2CyZV1U+SqeOGXpyE9hzhiWXYvpex3hO6lGwRBheBVJB99M++yXlk2I9mpCdTU1fTzLytFiKKVr8Xufo5yXo4vsGKKYAc0g2oUDW7kG5vLF9PHiZLsgZpIJWto7saGhGS3t5gcjC9GSia9Lqs+2fMaC4nKv9hsn6rTIKjAzlmxsLHh/0y3/fNGWgiHum7hcnFb6Mt3y0gJRujvHLerxPehZmbFqW8AvPRca+d6Tc0TnzYfMZTLfXuK/4phppHlYLD98ei7OumdKJOfKxSPvrO61Teq9snhj74Vpgp7XBXX2hDL5Wx4r1YXNE9PXYIpPeJs1woqjX24/P3t9karkqFGwGOnKUbOFXiC7s3gO5eM9VQiZwcRW+ay+lQSC7LmtK8Flo9iapqk4UCaYb5GBCvKgy/QSzIew5zB5UaPgIQmzjf2oSKhuJV4s6nFRPMR9W9QolAIJsAlJCQamyLHqTsVdkgoIIwpzGKhRKAEyX06rXlaXhLwPPVD7pJhEXDGSHi+kl0qNgofMcBFhkXnTpV5GgcfLOGAhj0quxVKKXfUp0yc+qS2C9s7eucvM4pneSWRXwL2XzMo2HSokCbXtBEjMihoFD7e9XBPLee9+a6ko3eZd/iEbTLhB3v929lhFn/iVfwwo08xd1xDJeQrF7149GOD5khnjKqiwmCe8ZtNmtE44UNwR4KIsCS1x5aOz8tIEAI+/V5v+nOkx91yAF460HG4TrkoXJ3HXl9QoWMDSzTKf5sYQY/FETdBzH6WXRSH43auFAZ4vfq0KP6TXnMQKqNTgBZFZEVqzrTivtXyW1LSdsK5EjUIMFH4zk/tAa4gNAyT39it5IJ29rJPXSphyLAeDCv8kLrMZeC3SpUXF4WuF6RSlCNQoJIgkt3wztQeVb9a3FHzuQRINmRI9SXl/1SiESJDnRaFFiDj6ZR5P37LNjXi/dnthgkIgM29yeUVFja+XUWCrR0ar0FGgNQGDpKbZJRxHaxR6Oa2MYKWzdTv8w1lIo9I2NEcTrysINQoFIB08u14YQyhb+AkvYdQ0Pn/vVHz5b9PNHzgDqUtq5vbfjFscmqawmbBYFt9JGmvqjlcX5U5UYtz674WidNJAfhfeZ2aluGzMWFVcJeutGtlzo+spWMSOJtkDuHSTzKtI6qUiXmPZwj6YQpd+rC3S26QYiu0WKibWjR9hLAepREdSuhnVKFhA3KFyoyBTevDgbDJeHC/JU6zEgemafcl4HxHRY0S0hYgWerbdTkR1RDTX/bsgal1xIp/WHqqMSAkcaI5UhRmkhiyB9k4pQ+JoKTwO4Dyf7fcy80j37/WINeWFfLlDfzILB9O1YxtDAYg1lVDBmcRWj5IcSmbyGjNPBWCPu0uIdAj7gKUFpq1r6kowHQys1/FDMIR72mQOAFsbZTOSbfOkKoZi87vQeF8dwvE3K8mQLs3DoHUrmoULJ+WLTWMK3yei+W730pCgRER0HRFVE1F1fX3v1Y2iIChAWCaS2DAAMGdtgyidhQ0AMb0W8QmoRWeuDSEtBN5eskWUbpPPUo5BXC70ypqeZUW7Hsd7WHa8bZaH+gCAcQs2+m6XLn968/PzCzrvw1NXFbSfDWQ+yY9O671Cnh+fuett3+0vfVBXpCJ/bDEKDwH4KICRADYCuCcoITOPYeZRzDxq2LBhEcnrSSmtYRwVLRm1mqAWQaZRkMaqkUbjbO2Q1662Fbm+c+85F7LasdQbLU6CouZKW8frA3z5S5nM56Fe2MKMGiuMAjNvZuZOZu4C8AiAk+LWlI0k19jjotAsS3JW65BCMOX4DiXlkq0wCkR0gOfrZQBkM1YSSqH+ykl+kTJr/OJ4QeL5DflrCptStgnF+txbeLsUl6qoT0hETwE4A8BQIloP4JcAziCikXCelVoA34laVz4UWwMsdMA40QPNBUqXdh/JnZtKuahODjZ6yEWNrTkQuVFg5it8Nj8atY5iKLYvcE9bJ/YdmP9+pt+jKBccWVW/G6d9vHsMKKhwXpIxC9zG+Pct7Z3o36cyZ7qwlcdZsE5bUZyTRzkOy2XeLls9qazoPkoav3tjSVH7nxrgTRA1j70r834wwe0ZcXvEs7iFxzf9ei3asCvwt3snLBMd4wOhV1kmU5fLClyp11oYvL5gk+92W4q5oFUKbeKJ6bVxS/BFjUKCMP3CSSNLlhJSY5TNS2lXs2z+QqEtMel8hnw8qcqNYtc9j4KgZU7jRo1CgjDdXZCE/nULe4/EhO59lOC8SbD0kkeNQoKQr6cgSxeny6Q43pM0XZKtRwZJDo8hj+NVOvdLSlIcRdQolDFxFj3ick8cAsQ+Ci33kmsS7CHJhjVuytIobGlswYOTVySutmJcroEXp9A8nClciETc7ZqsW5mVLcJB0myX3BVTf3WTMB5P2J43SXu3baIsjcKNT83FH95ciposHiY2Ig3lECUzVxcW2/BFYdwWaZO7eo3ZGIvZa5oyTVsLjGF039srCtrPy5Rl8cQFu2/iclG6RRvtf/ekrudSA5wUO1WWRiEV/TJpMYyktR9pA8BEAzvsPJS+SDbGC4pTUVzeN82lFAlWvNJhyEIipiyNguKQhIfZdO0qymsuxy4MWy5ZxxQKR41CCVJKIR8sKWMKIvwZzSGfoACS4mGjBKNGIUEkudZcKHHVthOQNVZio6EKG+k1JyVrytooJOUmhYWJF7gt5L7r3a2y2cPSsQ0T3QpdwktubJFpDwPprfXOui7H7q5stArXv5CSlJX3ytooJA3TNfvFBjxAXpvnvwKXKdbv8F+KMJNJS2Urr5lg7roGUbpeq81ZyJ89cZyKXVSo1Ji4eLPR4z01a63R44VFWRuFcu8iMNH/a0vtMkq//FLqN/dmm4mItAlz6MuK6dhEtkZFzaSsjULSsKT8VSzBiFE3buD0IQ3CxjDwfqhRKGMy10MuhGQ85t1IrzgJg/DGMXAzE1LuGcV0TLK4KWujkJB7FBplWfCVEDa6FJf7O5WNpHQ7lqVRsO9VkiH1xJFiY6FSKHuEMXcammShQrLV6myZPb1tT3AYBq/+5mx5I54xLvPEsWWMSYqJmd/SY2wpcsXGqChLo5Csx7abZ95fZ/aABmyCaUMVNj99fl7Rx1i9dY8BJcUjjTP00JSVgb9xwOdM3hfGuLLl3ZI+2jNWbSv6XNW1O0TpVtXb8dzkoiyNQorSqScXRrlff9KxpQBWSouyNgpJw/QYQDnGh0nCAkTGka5JkS2ZdE1tSyxVlDKSMlYgJRajQESPEdEWIlro2bYfEY0nouXu/yFh6yitW5k/FaVU8JUh2W+f1BDI0kk91crxnbLFEJoirpbC4wDOy9g2GsBEZv4YgInu91AIqywMe5DNeOwjs4dLBKX0/ma7lh7PioFmT9KW2TSxsF85tqSBmIwCM08FkDlydQmAse7nsQAujVKTCVaGPJC0bkdT4G/el3H5lkbR8Uw89OMXdYcCsKVAyMSrK5tG72+bd9nvKZJt4LLHAHLWa/buI7t/2eJMvbN8q+gYScCbbx1ZPIxenbchCjmRYdOYwnBmTgXS2QRguF8iIrqOiKqJqLq+vrDVpcIquq56bFZIR3ZYtz04DpB3Sv41j1eHqiOIFsMBxMIgmyH0FpBj36sNX0xEZLPVhbwLha4oZyPSelFdQ/C799zs9YbU2IFNRiENOyba93ll5jHMPIqZRw0bNqyo85Rn47Ab09dv64BbIaGNk95zEGajzdIGoRXY2lrOB5uMwmYiOgAA3P+hh71M/u1TJMi7Usr7iSjH/vVSWpDKFDYZhVcAXOV+vgrAy2GdKKkDzdnPXcBOhjMi6WVqz5ZCsguBMFtttrYIwyThj0NexOWS+hSA6QCOJKL1RHQtgLsAnEtEywGc435PFEl7VUwExPNi6/VLjXXSjZoX6QByz3TBeB+VUson05RC3lTFcVJmviLgp7MjFeJhZ5aYOAvrduK/DhocoZpoMD6mYOkb4VW1ZFOwZ5a38NzZLIuRlAQemJQtzIXsnj1XvQ6fGrGfu4/9/P3d1aJ0pTRobgqbuo8iw++hnrk6OAbKPf9ZKjtuEt4WD6abxAm7/F5479884epq5cKz1cnysHnkHZlReKtmkyid9F1J+jsAlKlRCIs4+1pt6Oe11SjaqitMConxL+9mK50MlYc9KZ9BhbI0Cn63N+vs0LCExIxxjwpLM0pqMEuorAuVcsknryFI2ozuYihLo5AvJXCfI8GG1koxJF2/l0IKp3J8zsvwknOiRkFAUtZWzRfjYwqWZpN48ponXaVGCwzE9H1OQu1axxRKnHnrdwIAHpi0Ir0t23O5q6XD8znYKyXOZ3v55t1575NN7wpP/KSX59YVIskatgjjGHmzY2DfStk+lhZoUlkL6namP0s9rmxtUUlXQFuxpftdaWkPXpXO62Biuqu1rcPekDBlaRRSeIO5ZWPF5u4CMlvhG+erUkhQrmwtoNcXdHtl3Pj0XNHx7CwqsnuWefEW8P91oMwFOVtwuCQwe033qmELPQYiSkzZ1d0tslUAn5vdvYLhpp0tgemahEu8epFeS7bKZdyUtVGQUognRxLI1iSuqsy/ZmRtrdlwuiQQ6ozmhGdUOYWsKAQ1CgKSUBssLMpF8MvRpyL/R8PaXCpgTKGAy7eKQgruuIynqeNJ+/17zM42fGypMbbZLCX80Y8G+QsWZ+wjs+cuy4FWTxZKa5O2+q+HGyW1dOYzSO+e6bts63MDqFEQUY5+7n0K6j4KQYgBxPfP3raOVdiaS1JD7q3viMtmw2tU22sS1CikeXtJ8KBze6fsTm/b05b+HHVMlUIK5GwvRGUh3UdCEWu2yVaoy+UN09QmG1h8/L01onTz13cPtEoLC+l9bi5g0LIY5hYQpmOvfrJQaNJnbWOWQdyex4vWzHgDQS4rwGsvShpjGJBWo+BiOrbLsiyB15JAmL1Hs1ZnrsTqz7rtwcuPAsDWxrasv6dYvHGXKJ3UG62QY29vkmk1xRsLZTF9vBz14UHClLJCfP76hrw1FEWIz6zpwWlppUNqWE2iRiEsbG4fhoR80M7+zEmCRtOYrrALpw1E3h1VyL01/TiIx6zMnlaEGoW8kXoX2F+oxDWjWdyNmyNhmOW2ONaN4eOZI36XVFNjNKa7lwq5F/LYRyGKiAg1Cnkj9UoJWUYGhb02phfZERpMqetgjG+O8ZphxJcSphe19NBSDbkKUtMtGBsagYW4z0aFGoW8Sb4fctyYeiFsaClIS8gktBxNe9l1GbJMpucSFHIvpF1O5j3Yon9u1Cjkydocg58pKgyN1Eq9Dx6dJltUZLvHQ+rFOcGD66NfXCA6nhfpJL9cy4A2uIOyud7DNxbkP5iajX/M6PZSMl0I2FA7NYV0cF0aSDJXHk5askV0nIcmB68w5+XeCctE6bxsaGgWpfvFvxeK0pnqQg0DNQp5srBO9kKYupdbd5v1Wqnb0f1wtxoOymVq5rfU42Ls9Foj5ytFwnTznCn0HjPVhfWmcHU0qVEohKVCb8IX58iCR0orHTrQnACktR9T3ivG11G2YAAyV96k8jhXMz9M93bTA4tJGGOKKyR21GMKhWDe+8heZLNVIoSIagE0AugE0MHMo+JV1BNbCwEpYQ5Aig1mjt9Th4kzD027pEY9phCqwRReiql1SGyYaR6X40EcrtHWGQWXM5l5a9wi/JDWfkzdSvNuo+G9YFKDk+ua0kahODlFIQ6AFn95ZQzTLsXGKiAW5LHxyWs6T6F0kBd8prqPzD4W4QZ8NTOHw4aaodRzxrRnjCk6pDPHPJjO91IymKYjJatLan4wgP8Q0Wwiui5uMZl4PVSyYWqK/4TF+YdeyMa2EGMyif3ScxQ+qRW0crcowit1Jgo9Xp4QDnYvMLSAzdOz1orS7Skg1tKPnpkrSmeq+yjlxZTrNq7cKouVFSY3PTcv0vOl3gGpp5dJbDQKn2XmTwI4H8D1RHSa90ciuo6Iqomour6+Ph6FAhasN1MISD0vpNSHaBSkZXRHzgCDlPHfXt5ZLuvlNPU8SCslhSANDidtveYy2os2yAo878qHpUIuw5oyCjXCPDKJdUaBmevc/1sAvATgpIzfxzDzKGYeNWzYsDgkijDV7DPufWTBQLPcg6sYNXZh6lISsN5TGlMtxwRdshgdUxBCRHsR0aDUZwCfByCbDWIZpsYCjK9yFaJVEMfEyZku5ZIqSVVe2LBwTeTeR/Ffcllhm/fRcAAvuYO0VQD+xcxvxispZkz7jps9XMaxzQzOmjMu9mCq1WOqoI2CBEm1l3J3SWXmVQA+EbcOExi7l4afiTbDs5i9iGPi5EiY8vTI5cFlg5eSFFPeaDZ0HzUJB7HFYS5yTV5L0H0uBazqPoqDXYZXNkqtxGWqEHi/VhZSQMqd4xYbPZ6XXFpXbHEGMmeuyp7uK2NmAJDbw5oNZgZxwyC1UJD0cci1QpsNLYXnZ8sWpMqlVXolFlxyTnKtEpjJeyuzOyhMWCzzfguDsjcKe1plSzpKaWhyHg5TLYUkvBApcnmTrNvhFJCmC/GV9fG7LAaRiuMkHWNqbs9RC0/Q8yAfaC7udxvYladRWJjDRXnu2oacxzh86F55nVNK2RsF05PDwlzGshCOHC5dYrF4Og11F0ixoSslF6mBYVODswm45DRJqtBEjc1dZmoUjMc0cQ5oi3GIcpzK9Ipc5eiSmisPTc+sDRP5mELOEtJ68jWAUmeLsndJjQPTmd497crwqmYJqHblKgRSOSKPsWNmklScpJSJYynlKC5sGFOQYuq+JGGgOQkapahRMFwdbelw+oQ7hDW6dmGMGunxTJJr0DOTHU2yflVTk9d2t5gdDwqD1KWKByJzZE3qOHEYwnzjKeXydEv9bspFOU7y1ShdPEtjH8WA6Uy/+rH3AQBP5YhRk3qpb3+lRnTci++fJkq32mCcmKv/Piuv9FOXycKOmHrJcw7KWkDqPj8wKfsCMFsanQHpXDGSGl1D+GCIC8oA/u/Fj5/NL/7PfW+vyPr7LS85q/s9+s7qrOlSj8tEYRwwU8uAAsBbwjAz0jOmtD2S45ola4qce8xw4VnzQ42C4eNt2iVbNSxVML67QhY7Z4lw5afNwvNLkK6wJSXVKvNrKbx8/alGz/XlEw82erxCkZZPqVbPmm2y5V5nFXhvhu7dV5Ru6R3nY/yPeoQdMx6HK8XU5bLKhDSoYLEmYdJPzkh/TrlR+/Gd0w7vPmeWms5efSvTn02Fgpn60zNx83lHiY6VL2VvFOJqmXLGf2PHjeGC8l1jwq8rbK9+lb22FdOK22dAn8J3Noi0EEitW13IQPKwQf3EaY/40N6idH2rKvCxDM81kzVwL7kXXcrvvMVW9IbvI8vPAwb3T3/OpvBD+8jSecm10NSh+w9EZUjeLGoUYrIKqQc97IHDKFZuyndxHWnBV4x2WxyXpHc39YIXMnaU7VpNjj2ENa4lnadg29hChadQNh2aJc5LVaMQU/an3i/bHvRCkLcU8qsNm14nOQ7EeZM2mIUsjhNMklxYg0hlYVRX4u3Hz1Yv6VlpCVbnTWXMTTdEyt4oLNu0W7zwjMkbtdudSb19T5uxY8aFNFcamp1r3dIoy+8kuV8Gke81NApn2IsHNhOQhVt2Cdf4iOh58FYUs53SW9hnbUV5EtY1NIs0rKrfg5oNO0ONVRZE2RuFrz86E3e8tihnut2tHXhOGPNli2Cw95N3jAcgCy5WjDEKquhIPYUk1Ao9nr7/rw8Cfxu6d+9+3JsE3i47m9uxzcewnnrE0F7bpPkoGfzPluYzH90//flvk1eJKh23vOREiH94yqqcaZdvbuxxLR8fHjxOkGmULjjuAN90QTHA9u6Xf8zMf81ci+V5LIwjLSilb8HmRpmzxew1vQfrDxzcH30qu4vFByf5e1AdfcA++OShQ9Lfx2S5b//jcXo4+54pIm0zV2/HhfdNw18Dzh8mZWkUHr7yxB7fp2asnjXn1nNR86sv9NjW1NaB/3vH/8b/+pJje3x/IOBG/vkrI7PqWphxzhSZtZBLRh6Ieb/8PFb85vysx7vipEMCf5uzdkfWfYNIxVvZd2D3QG7Q/IRxN3wW79x8pui4+w7s7RXj5/10wXEfxgXHfTj9vaW9E81tPWvXz37nFJx51Id67RtUmTvxI90vNzPj928uyak3FejOj9u/2P08LNvSKOqLfz8Pb6K/TFzeo7A/fGiwUci0g1ee/BEs/vV5+MP/HJ/e1tXFaPFUTi4deWD68/VnHiHSNMbzTi3euAtTAiodK397QdbjXBhgtDKZ+tMzMfWnZ2KVz/EahPNlUnGpUiy783xMufnMHkbBb1nTpXeeh1e/fyqOOXCf9LbFPt6BS+44D++NPgsXH39gr9+8nHL4/pj0kzOw5I7zRLrDxqrQ2XGR2YWz3169C6jJS+sDlyvs36en58xTs9b5pss1bhpUK8tcvrKqogKDBd41VRXBNr9uh6x2lknKGHgLm6Aa+MC+VaiqNDvkO7BvVY8mNXPvAekBfXp7MgHBEwUHelwGX1+wCfMFS2dKJx1KG3n5OJKMW7AR+3ruf7bnKjMeFRFhQN9K9PHcl/aurh6Gq8pTKErH+vtl5HnQMqW5PGayPS9eQ1hZSTho3wG+6aTjKJn3pm+VrI7cr6r38+U3sa9/n0ocuO+ArBUIANirXxUOEwS3q6qgSCaxlmVLoRAenrJS7OMdRKHeNO0Zg4/ZBsczfws6pbTJnonfNUTdb51ZrkizNaiw8BY2f3xrCQ4flvsFbcu5zrQDM4sKqXyejb36VhU9e9w7mNrRyT0MbbFmvK2jCzNXbyvyKL3xlrtBtkWa34DZMas4Ig6ERdkbhRM/MgTHepqBQays34NvnnqY/48Zz0O2bptC6LXQvYHnb0OBRiEtwfNCBRkpZjYeAwoo3LgGvbhem1u7rQk/O//onMeSthQA8x5A3zx1hDgtC2R2dDLaPNfjzd5Cys3Za3egpd38AKnXMyvoueroYnG0XpPk8zwUSlQhL8q++2jUR4aAiFCTYy0AAPjapw/FH99a2mt7ZqF4/ZlHYOz0Nb3SZd7TJ6bXijTmCpnhPd7ijbkH+J6YXota4czZTI1+z+WLc+qyzvzMdrxC0nk1PFe9LrD/OvN4e1r9B/W9NcZRHxmCc47uPR6RyWvzN/TodgpiV0uHaFGa3a0d4rz51mcPx/05QkgAzjUHOTJ4n9knZ63BrmZ/r6fMZ1uiUfosSI+XSvdWTXeYi6CWwtj3akWhXsa+V4u56xrE587F+h3NRp5tGyjLlsJRH+6eqfnFkQfiC8f6xxC58PjuQa8rTjrUdzAUAD592P49vntnMKa44LgP4/iDB/fYdtvL/nGPzjm6p55MQ3TZJw/y3e+2l2t6GJALjz8AX/30ob7ppHjTXnbCQbj8U04r6Efnfjy9/fnZ632POXRQvx4D0vmcOzPdyYfvB8DJxy96BkLvGb8M1Wt6DpofNMTpa+7r6Ru/7eWaXgPIhw3dCwftOwBXf2ZEetvPLjjatyVy3EGD8ZVR3S3AhXW7fK/l2587DMMz7v9fJi73vcaLju85qJp5vBvOOqLXdQDA4IF9MMozOH7xJ5z8uOyEns/FbS/X4K43/AfNRx7Svf8f3lyKv03pjqWUOh4AnP7xYVk1pvhYxkxprz7A6Q/3I/N4F7mDsiP2H9gr3VpP33zQjPU7xy3GkzNzV6J++UoNXvqgLv09W+u+0Gc2ReYYZWa6S0/IPhCd4junfVSUrljI5rDDuRg1ahRXV1cXtG9TWwcqKyg9aNTY0o7OLvYt+He1tGNQvyoQEdo7u9DW0YW9fAaFm9o6MKBPZTpdc3sn9unf++H1uigynBfG77zNbZ1o8njWVBBhUP+qHgOBgOOB09jSka49MYAhA/v2GtTr6mLsaHIG1SsrCH0qK1BVSb0Gzjo6u7CrpSPdRcQA9unfx3cgbvueth5dSXv3r/IdiNvZ3J4ejGMA/aoqMMgnb5raOtDU1pluDfSpqvDNw8xrrqqowD4DqnoV6C3tndjd2pE+XhcDg/pX9XIOaO/sQkcnY4Bb+2/r6AKDfa9lS2MLKj3n6d+n0vd58F5zn6oKDOhT2cOzxS8d4NznIT7ODrta2tGvqgL9qirBzNjV0uHrcNDe2YVGz/2rqqjA4IH+haj33H2rKtDfR2NHZxdaOrrQ6glAOGRg3x4zelPUN7aigoDBA/qgvZNB1NsRI3Ut7Z4oqf37VPo6WmTmTVBed3Yxdja3p6+5rbMLwwf176WxtaMTO5vb0/cv6LlhZtQ3tvZ4h/YZ0Mc3b3Y0tfdouQTlTea1VFX6O4yk7nNzWyc6uzjwHSgGIprNzKN8fytXo6AoilKuZDMKVnUfEdF5RLSUiFYQ0ei49SiKopQb1hgFIqoE8ACA8wEcA+AKIjomXlWKoijlhTVGAcBJAFYw8ypmbgPwNIBLYtakKIpSVthkFA4C4J0KvN7d1gMiuo6Iqomour7eXPweRVEUxS6jIIKZxzDzKGYeNWzYsNw7KIqiKGJsMgp1ALzOwge72xRFUZSIsMYllYiqACwDcDYcY/A+gK8yc+DMESKqB9B76rCMoQBkCyTHh2osHtv1AfZrtF0foBrzPe9HmNm3q8WaMBfM3EFE3wfwFoBKAI9lMwjuPgX3HxFRdZCfri2oxuKxXR9gv0bb9QGq0eR5rTEKAMDMrwN4PW4diqIo5YpNYwqKoihKzJSzURgTtwABqrF4bNcH2K/Rdn2AajR2XmsGmhVFUZT4KeeWgqIoipKBGgVFURQljRoFJStU6NqXiqIkEjUKMUNEZxFR7pXi4yO9uoetBoKIhrpRdq3USES/J6LPuZ+t05cUiOhLRDQkd8p4IKJBns+Jvc8laRSI6NtE9CARRbN+XQEQ0deIaDaAMwG0x60nEyK6wtX3GyK6EQDYMq8ENw/nArgbwP8Bdml09b0N4Nvun1X6AICIvkVEz6aMlo0Q0deJaAaAzwJoiVtPJkR0ORHVALiLiP4ARHefQynrmLkk/uCs514J4CsAlgOYCuCrAPrHrS1DYx8APwGwA8DJcWsK0DkKwHsAToYzRX4egGtS12CBvioA3wcwBU5BUQkn3MkpcWtz9Q0E8DsAEwCcAuB0AH8EsHfc2jJ0fgHAIgAvALgZwBCL7jHBqbR+E0AngE/HrSlA55EA3gFwkvt9GoAbI8ib0Mq6kmgpEFF/dugEMAfApwE8BOA0AEfHKs7Fo7EdToynJwGsIaK+RPTfRCRbvTtEfZ6vRwOYyMwzmHkrHK2/JaLB7D6VccLMHQDGMfPpzDwNTiDFagBWxFJn5iYATzDzOcw8HcAQAKcz8+6YpYGI+nm+zoYTa+yvcAJQng7E35ohon7uu9IFYBaAZwC0ElEFEV1FRLG+0xl5eDCcStNC9/sjAG4lohNCOnfoZV3ijQIR3QrgTSL6AREdy8zLmXk7gOfhWNTPxd0P6dF4AxF9HMAbcNaOeAPOjb0MwFgi+rmbPtL7kqHvEABLAZzvWfmuC8AuAD+KQ597zl8R0YWp78y82t3+KQAvAugHx3DdFodGH32LPZ//DWBA3F00RPQzAC+69/lYZt7KzBvhtLjqAIwiohFu2lj6xDM0Hs1O/LP/AHgNTuF7CoDHiOh3bvqo77NX3wgAGwCMAHCum2eDAayE804b1RdVWZdoo0BE18Cp6fwvgGFw+r9HAIBbI38BwIkAPpmxX2QPfIbGoXC6EYYCeAXOw34eM38dToH7EyLa360hxaXvrwAWwylob3bHFT4Ep3l6ERHtFbG+/YhoDIAb4BT6mfG61gM4m5kvgtMF8gMiOjAqjQJ9IKJ9ALwNYHgUmnzOf5g7tnEsnPGXIwFcnRoYdfNqAoBBAM5xt0XaWgjQeA0RDYQTJPNhAJcy83cBXOnqj/I+++n7vmv8XwVwAZwu148DuA7A5US0ryl9kZZ1UfW9hdSv9ksAl7vf9wZwJ4CnMtL9CsAPAVwI4HoLNP4WTtcCkNEHCOApAEfGrO8uAI+63wcDGOl+HgHgPjhjIpH1OcNpAVzgfn4dwP+6nysC0r8A4FQL9FFGuqcB3OB+roz4ORwM4Mee71+A0yXY36sTwFUAbgNwNYDRFmj8F4C+cPrP+/jc58jGkAL0PY3usY8+AI5yf6sA8DcA+5l4V6Iu6xLbUmA3FwB8w/2+G8BfAHyUiM7wJH0TwC1w+vr6RigxSOO9AI4mojOYuQUAiKgPEd0PYB8Uvj6EKX33ADieiM5i5p3MPJeI+gK4FUAnM7d79otCYyucgTTAeTG+TUQHMHOXtxZERP2J6F44/fdZQ65HpI/JodL9bRyAK9x9OqPSR0TEzDvhPP8pauCMw/RJ6XS3fwDHMNwVlb4cGg8G0I+ZO9mpDYOIBhDRn+EUuIti1ncgHOeBLve9WEJEe8Np1Qxk5u0m3pWoy7pEGAUiOtLbN+d5iO8CcDgRneZ+3wrgnwA+76YbBuAPcJp3RzDzvZZo/IdH46Vwmp2dAL6cMhQW6DvbTfdJOF0fAPCzMLQFaUzBzLvdF/N9OP3fd7jbUwXvF93tAHARMzfYos9jADYBeCDsPvBMjakChZkbPck+DWBdapubj30B/BmOJ83hzByaYShEo7vfmQAmul8vdAtqm/QdBqesAYDvFHjus4jow57v0Zd1YTa5DDSbzgUwE04/fFVGc6rK/Xw9gJme364HcJP7uR+A/S3V+BP381FwVkGyVd/+AIbHmIcV7ueU1mEAlgD4GJxBx+Pg1BoPsVDfyXC7OBByl1GeGn8M4Gb38ykAjnU/D7ZU42cAHAanFXiQpfqOdD8XVN64x6gB8AScQl36nhov60J7AIq4MSlf/l/D8cH9UsbvlZ7PB7j/34ZjST8LZ1DqpwnQeLPl+qzKQzjN8dT3v8DxiJoL4FPlqK9AjXu7n++D0yc9Bo6zwzGWaxwflkYb8hDOmMnf4Sw/7N1e4T23+z/09zSUB8HQzbodwO883z/n3ryUxb4HTrfLCACHA/gunKb7z1VjMvTloXEqgE+5L/BFAFYjRKOaJH15ajwRjofRBjhuxzeqxvj1wWkBPQ5gXzhjAVcCOAKuIwqAP0X5nlqzngIR3QCnK+B9Zh7j9qul+jVPBFALZxbweDheHrcBuJ2Zd3iO0ZeZ28pVo+36TGgkoo8B2MLh9Sdbrc+Qxu8BeJaZt5Wrxjj1ec49g5kfJaL9AbwEx5njxwBaATCABvf77wD8MrL3NKqaQg5LeTWAGQDOg2MBfwHHal4Kx3XuKDi1sEvg3KADPftG4t5nu0bb9RnQWFXu+gxo7Ksa49UXcO4BcAzSCnS7nQ4CsA3AJzz7RvOeRnESQUb9A8Bl7udRcLw3Rrvf9/KkO9y9aQe636P0l7dao+36kqDRdn2qMfn6fM59J4Cb4HQbbQZwrSft/QA+H/X9i9Ul1eP29QGcvlgwczWAdwEcRkSnMvMezy7fgGNVt7tpQ+/7sl2j7fqSoNF2faox+fqynHsanFnSBwD4KYAvENHFRPQLAKfCnYsRxf1LEXXckB4x77l7Cvi7ACo8PrgLAWyEMzkE5ASMmwfHcn+PQ/LlT4JG2/UlQaPt+lRj8vXlee51AE5k5ifgzIT+LIBD4cy5WZ/vuYulV5yWMCCiUwBcA2ApET3M7oQPIqpiJ+Llcjg+ul8honeZeT0RDQfQ5B5iGYDvshNxsiw12q4vCRpt16cak6+vwHN/CM68FjDz20Q0mSOML5ZJ6C0FIjodTpC1t+FY4luI6PNAOgQyADTCmUnZD8DdRNQHjpvWVjfdgpAfcqs12q4vCRpt16cak6+vyHOnw77HaRCAaLqPTgTwLjM/BWdQZTiAK1zLDCK6E07gq51wXLKGwMm0nQDGRqAvCRpt15cEjbbrU43J12d73ogw3n1ERCcD2M7My9xNSwGMJCfM7QYi2g0ndMKlRDQJTr/daGZe6e5/DRwPgEa/45eDRtv1JUGj7fpUY/L12Z43hWJyAYh9iWgcnMkel5MTLRBw+ud2AXiciF6AE51xLoBBzLyMmb/KzCvJHZ1nJ+JgWA+Q1Rpt15cEjbbrU43J12d73hSLsRnNRHQQgP+GkylHAniHmV93f+sLx71qODM/TUQXAPh/7CyMAiKqiKIfzXaNtutLgkbb9anG5OuzPW+KpSijQETfgBP//wNm3kXOOr8VcPxtCcAYZt7gs98vADQw818LPnmJaLRdXxI02q5PNSZfn+15Y5K8u4/I4QC3j+wqAF8D8BARDWXmFnYWLZ8AZxDlrIx9P0vO8o6fg7PmaijYrtF2fUnQaLs+1Zh8fbbnTVjkZRSIqJKdpsUgAHXMfDaA78GZ8TcmlY6Z34UTUOooIhpMRHu5P60CcCszf4GZaw3oT5xG2/UlQaPt+lRj8vXZnjehwrJ4HZVw1hb+PYDTAVwMYKzn9wo4K0ud7tm2N5yVnGbBielxsORchf7ZrtF2fUnQaLs+1Zh8fbbnTRR/OVsK5EzImA2nibQCTvCodgBnEtFJQHqyxe3uX4oLAfw/APMAHMchTte2XaPt+pKg0XZ9qjH5+mzPm8gQWM7PAbjS8/1BOM2oqwHM9ljPDwN4FsAId9slAE6LwrLZrtF2fUnQaLs+1Zh8fbbnTVR/kowaCGdKdqX7/WtwVyiC44P7A/fzKABPxXIRlmu0XV8SNNquTzUmX5/teRPVX87uI2ZuYuZWZu50N52L7jgd3wRwNBG9BuApAHMAZ9Q+13FNYrtG2/UlQaPt+lRj8vXZnjdRIQ5zQU4oWIYTz+MVd3MjgFsA/BeA1cxcB0Qb+ztJGm3XlwSNtutTjcnXZ3vehE0+LqldcBay3grgeNdi3gqgi5mnpTIpZmzXaLs+wH6NtusDVKMJ4tRne96ESz59TQBOhpNh0+BZNs6mP9s12q4vCRpt16cak6/P9rwJ8y+vMBdEdDCAKwH8iZlb8zE+UWG7Rtv1AfZrtF0foBpNEKc+2/MmTIwFxFMURVGSTxSL7CiKoigJQY2CoiiKkkaNgqIoipJGjYKiKIqSRo2CoiiKkkaNgqIoipJGjYKiGICIziCizxSwXy0RDS1gv1vy3UdRJKhRUJQMiEgcE8zDGQDyNgpFoEZBCYVCHn5FSTzkLMT+EziBz+YD6ATQAuAEAO8S0QMAHgAwDEATgG8z8xIiuhjALwD0BbANTnjlAQC+C6CTiL4O4AcAlgD4G4BD3VP+kJnfJaL94UTZPAjAdDiLvmfT+W8AhwDoD+AvzDyGiO4CMICI5gKoYeavFZ8jiuKgM5qVsoOIjgXwEoDPMPNWItoPwJ8ADAVwCTN3EtFEAN9l5uVE9Gk4cfXPIqIhABqYmYnoWwCOZuabiOh2ALuZ+W73HP8C8CAzTyOiQwG8xcxHE9F9ALYy86+J6EI4i7oPY+atAVr3Y+btRDQAwPtwloHcRkS7mXnvMPNJKU+0paCUI2cBeC5VELuFLtxtnUS0N5yuoOc84fL7uf8PBvAMER0Ap7WwOuAc5wA4xrP/Pu5xTwPwJfe844hoRw6tNxDRZe7nQwB8DE4LRVFCQY2ConSzx/1fAac1MNInzf1wgqS9QkRnoOdavV4qAJzMzC3ejfmsyeIe/xwApzBzExFNhtONpCihoQPNSjnyNoAvu/37cLuP0jDzLgCriejL7u9ERJ9wfx4MIBVP/yrPbo0ABnm+/wfO2ALcY4x0P04F8FV32/lwFokPYjCAHa5BOApOOOcU7UTUJ8d1KkreqFFQyg5mrgHwGwBTiGgenPGETL4G4Fr39xo4i7MDTsvgOSKaDWcRlhSvAriMiOYS0ecA3ABgFBHNJ6JFcAaiAeBXAE4joho43Uhrs0h9E0AVES0GcBeAGZ7fxgCYT0RPSq9bUSToQLOiKIqSRlsKiqIoShodaFaUmHHHNib6/HQ2M6unkRIp2n2kKIqipNHuI0VRFCWNGgVFURQljRoFRVEUJY0aBUVRFCXN/wcY18ioppuGSgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.loc['2019-5-1':'2019-5-30']['count'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
       "            ...\n",
       "            3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n",
       "           dtype='int64', name='created_at', length=865)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['2019-5-2'].index.weekday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>created_at</th>\n",
       "      <th>weekday</th>\n",
       "      <th>weekend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:00:07</th>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:01:07</th>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:02:07</th>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>2018-11-01 00:02:07</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count  res_time_sum  res_time_min  res_time_max  \\\n",
       "created_at                                                             \n",
       "2018-11-01 00:00:07      8       1057.31         88.75        177.72   \n",
       "2018-11-01 00:01:07      5        749.12        103.79        240.38   \n",
       "2018-11-01 00:02:07      5        845.84        136.31        225.73   \n",
       "\n",
       "                     res_time_avg           created_at  weekday  weekend  \n",
       "created_at                                                                \n",
       "2018-11-01 00:00:07         132.0  2018-11-01 00:00:07        3    False  \n",
       "2018-11-01 00:01:07         149.0  2018-11-01 00:01:07        3    False  \n",
       "2018-11-01 00:02:07         169.0  2018-11-01 00:02:07        3    False  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['weekday'] = df.index.weekday\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>created_at</th>\n",
       "      <th>weekday</th>\n",
       "      <th>weekend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:00:07</th>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:01:07</th>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:02:07</th>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>2018-11-01 00:02:07</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count  res_time_sum  res_time_min  res_time_max  \\\n",
       "created_at                                                             \n",
       "2018-11-01 00:00:07      8       1057.31         88.75        177.72   \n",
       "2018-11-01 00:01:07      5        749.12        103.79        240.38   \n",
       "2018-11-01 00:02:07      5        845.84        136.31        225.73   \n",
       "\n",
       "                     res_time_avg           created_at  weekday  weekend  \n",
       "created_at                                                                \n",
       "2018-11-01 00:00:07         132.0  2018-11-01 00:00:07        3    False  \n",
       "2018-11-01 00:01:07         149.0  2018-11-01 00:01:07        3    False  \n",
       "2018-11-01 00:02:07         169.0  2018-11-01 00:02:07        3    False  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['weekend'] = df['weekday'].isin({5,6})\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "weekend\n",
       "False    7.016846\n",
       "True     7.574989\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('weekend')['count'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "weekend  created_at\n",
       "False    0              3.239120\n",
       "         1              1.668388\n",
       "         2              1.162551\n",
       "         3              1.086705\n",
       "         4              1.155556\n",
       "         5              1.136364\n",
       "         6              1.000000\n",
       "         7              1.000000\n",
       "         8              1.000000\n",
       "         9              1.080000\n",
       "         10             1.239011\n",
       "         11             2.031690\n",
       "         12             4.195845\n",
       "         13             6.668042\n",
       "         14             8.260503\n",
       "         15             8.934448\n",
       "         16             8.466504\n",
       "         17             6.784996\n",
       "         18             6.717731\n",
       "         19             8.655913\n",
       "         20            10.536496\n",
       "         21            10.846906\n",
       "         22             9.034164\n",
       "         23             5.946834\n",
       "True     0              3.467782\n",
       "         1              1.741849\n",
       "         2              1.161826\n",
       "         3              1.050000\n",
       "         4              1.076923\n",
       "         5              1.333333\n",
       "         6              1.000000\n",
       "         7              1.000000\n",
       "         8              1.071429\n",
       "         9              1.144928\n",
       "         10             1.254111\n",
       "         11             1.992958\n",
       "         12             4.031889\n",
       "         13             6.905772\n",
       "         14             8.851321\n",
       "         15             9.858422\n",
       "         16             9.420550\n",
       "         17             7.334743\n",
       "         18             7.342150\n",
       "         19             9.270430\n",
       "         20            11.173609\n",
       "         21            11.695043\n",
       "         22            10.419916\n",
       "         23             7.025452\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['weekend',df.index.hour])['count'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>created_at</th>\n",
       "      <th>weekday</th>\n",
       "      <th>weekend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:00:07</th>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:01:07</th>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:02:07</th>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>2018-11-01 00:02:07</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:03:07</th>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>2018-11-01 00:03:07</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:04:07</th>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>2018-11-01 00:04:07</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count  res_time_sum  res_time_min  res_time_max  \\\n",
       "created_at                                                             \n",
       "2018-11-01 00:00:07      8       1057.31         88.75        177.72   \n",
       "2018-11-01 00:01:07      5        749.12        103.79        240.38   \n",
       "2018-11-01 00:02:07      5        845.84        136.31        225.73   \n",
       "2018-11-01 00:03:07      9       1305.52         90.12        196.61   \n",
       "2018-11-01 00:04:07      3        568.89        138.45        232.02   \n",
       "\n",
       "                     res_time_avg           created_at  weekday  weekend  \n",
       "created_at                                                                \n",
       "2018-11-01 00:00:07         132.0  2018-11-01 00:00:07        3    False  \n",
       "2018-11-01 00:01:07         149.0  2018-11-01 00:01:07        3    False  \n",
       "2018-11-01 00:02:07         169.0  2018-11-01 00:02:07        3    False  \n",
       "2018-11-01 00:03:07         145.0  2018-11-01 00:03:07        3    False  \n",
       "2018-11-01 00:04:07         189.0  2018-11-01 00:04:07        3    False  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "created_at\n",
       "1     6069\n",
       "2     6029\n",
       "3     6044\n",
       "4     5994\n",
       "5     5960\n",
       "6     6004\n",
       "7     5986\n",
       "8     6030\n",
       "9     6032\n",
       "10    5982\n",
       "11    6014\n",
       "12    6034\n",
       "13    6016\n",
       "14    5930\n",
       "15    5446\n",
       "16    5582\n",
       "17    5739\n",
       "18    5876\n",
       "19    5869\n",
       "20    5920\n",
       "21    5917\n",
       "22    5965\n",
       "23    5997\n",
       "24    5978\n",
       "25    5997\n",
       "26    5988\n",
       "27    6057\n",
       "28    6060\n",
       "29    5205\n",
       "30    5164\n",
       "31    2612\n",
       "dtype: int64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(df.index.day).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(['weekend',df.index.day])['count'].mean().plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>weekend</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.239120</td>\n",
       "      <td>3.467782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.668388</td>\n",
       "      <td>1.741849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.162551</td>\n",
       "      <td>1.161826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.086705</td>\n",
       "      <td>1.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.155556</td>\n",
       "      <td>1.076923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.136364</td>\n",
       "      <td>1.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.071429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.080000</td>\n",
       "      <td>1.144928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.239011</td>\n",
       "      <td>1.254111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2.031690</td>\n",
       "      <td>1.992958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.195845</td>\n",
       "      <td>4.031889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>6.668042</td>\n",
       "      <td>6.905772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>8.260503</td>\n",
       "      <td>8.851321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>8.934448</td>\n",
       "      <td>9.858422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>8.466504</td>\n",
       "      <td>9.420550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>6.784996</td>\n",
       "      <td>7.334743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>6.717731</td>\n",
       "      <td>7.342150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>8.655913</td>\n",
       "      <td>9.270430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>10.536496</td>\n",
       "      <td>11.173609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>10.846906</td>\n",
       "      <td>11.695043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>9.034164</td>\n",
       "      <td>10.419916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5.946834</td>\n",
       "      <td>7.025452</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "weekend         False      True \n",
       "created_at                      \n",
       "0            3.239120   3.467782\n",
       "1            1.668388   1.741849\n",
       "2            1.162551   1.161826\n",
       "3            1.086705   1.050000\n",
       "4            1.155556   1.076923\n",
       "5            1.136364   1.333333\n",
       "6            1.000000   1.000000\n",
       "7            1.000000   1.000000\n",
       "8            1.000000   1.071429\n",
       "9            1.080000   1.144928\n",
       "10           1.239011   1.254111\n",
       "11           2.031690   1.992958\n",
       "12           4.195845   4.031889\n",
       "13           6.668042   6.905772\n",
       "14           8.260503   8.851321\n",
       "15           8.934448   9.858422\n",
       "16           8.466504   9.420550\n",
       "17           6.784996   7.334743\n",
       "18           6.717731   7.342150\n",
       "19           8.655913   9.270430\n",
       "20          10.536496  11.173609\n",
       "21          10.846906  11.695043\n",
       "22           9.034164  10.419916\n",
       "23           5.946834   7.025452"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['weekend',df.index.hour])['count'].mean().unstack(level = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(['weekend',df.index.day])['count'].mean().unstack(level = 0).plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
